Use of remote sensing data to

determine stress factors for the SALUS

model

1URS SCHULTHESS 2ZIA UDDIN AHMED

3JOE T. RITCHIE

1,2 CIMMYT Bangladesh, House 10/B, Road 53, Gulshan 2, Dhaka

1215, Bangladesh

3 Professor Emeritus, Homer Nowlin Chair, Michigan State University, East Lansing, MI 48823,

Forcing of a crop simulation model with

ground cover derived from remote sensing

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Stress detection with remote sensing

• Thermal data for water stress

• Optical data

– NDVI

• GreenSeeker & GreenSat, use of a well fertilized reference strip

– Red-edge based indices for chlorophyll status

– Fluorescence for rate of photosynthesis, RUE

– Photochemical reflectance index (PRI) for rate of

photosynthesis, RUE

3 Study sites in the delta region of

Bangladesh

Model for irrigation scheduling

Ground Cover (%) from remote sensing

Daily Weather • Tmax • Tmin • Solar radiation • Precipitation

Forecasted irrigation need

(yes/no)

Water table depth

MODEL

Wheat and maize

Use of UAVs for Research

RGB Sony NEX-7

Wavelength

(nm)

1 ILS -

2 Green 525-535

3 Red 675-685

4 Red-edge1 705-715

5 Red-edge2 735-745

6 NIR 795-805

Channel Name

MiniMCA (Tetracam)

Thermal camera Optris PI 400

Altitude(m) RGB MiniMCA Thermal

15 0.3

65 1.2 12

100 5

Resolution(cm)

Ground cover and canopy temperature on

March 2

DRY

DRY

DRY

Ground cover and canopy temperature on

March 14

DRY

DRY

DRY

Ground cover and canopy temperature on

March 27

DRY

DRY

DRY

Ground cover and canopy temperature on

March 27

Dry Wet Intermediate

Dry

Dry

Wet

Wet

Intermediate

Intermediate

Drought stress of maize as due to a pan layer

Daily maximum and minimum temperature

Daily average temperature and solar radiation

Temperature

Solar Radiation

Temperature reduction factor to control leaf

expansion

trfegro.dat 7 -99 0 6 0 8 0.05 23 1 34 1 43 0 99 0

Daily and smoothed photothermal quotient

Photochemical reflectance index (PRI)

Source: Peñuelas, Josep, Martin F. Garbulsky, and Iolanda Filella. "Photochemical reflectance index (PRI) and remote sensing of plant CO2 uptake." New Phytologist 191.3 (2011): 596-599.

Plot size: 20 by 25 m

2 Sampling areas per plot: 1 by 1.2 m

Leaf Size Analysis: Length and width measured with

Google SketchUp: https://www.sketchup.com/de

Average leaf area by V-Stage for 3 sites

Leaf area index vs V-Stage of each sampling

area at Patuakhali in 2015

PRI changes with NDVI (amount of

vegetation)

Examples of PRI/NDVI vs Leaf Area

(individual leaf)

Barisal, March 12 Leaf Area [cm2] = 172.85249 + 10.113794*PRI/NDVI R2 = 0.54

Kalapara, Feb 28 Leaf Area [cm2] = 148.73649 + 6.9412593*PRI/NDVI R2 = 0.48

Examples of PRI/NDVI vs Leaf Area

(individual leaf)

Kalapara, March 13 Leaf Area [cm2] = 329.79811 + 6.8204081*PRI/NDVI R2 = 0.36

Patuakhali, March 14 Leaf Area [cm2] = 210.53355 + 1.9893104*PRI/NDVI R2 = 0.14

Prediction of relative leaf size as function of

(PRI/NDVI). Each point represents the average

measured at a given acquistion date

Y = 0.83 + 0.53*x R2 = 0.55 Y = 0.83 + 0.68*x R2 = 0.37

Recommended reading

• Magney, T.S., Vierling, L.A., Eitel, J.U.H., Huggins,

D.R., Garrity, S.R., 2016. Response of high

frequency Photochemical Reflectance Index (PRI)

measurements to environmental conditions in

wheat. Remote Sens. Environ. 173, 84-97.

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…. At last, dancing in the rain

With great support from: Amirul Islam, Mohamed Atikuzzamman, Mustafa Kamal, Rokon Golam Morshed, Saiful Azim, Shah Al Emran and Shahidul Islam

Thank you

for your

interest!